COCO-Search18 fixation dataset for predicting goal-directed attention control von Chen, Yupei, Yang, Zhibo, Ahn, Seoyoung, Samaras, Dimitris, Hoai, Minh, Zelinsky, Gregory

Volltext

Are Search Templates Target-object Reconstructions? von Ahn, Seoyoung, Adeli, Hossein, Zelinsky, Gregory

Volltext

Generating objects in peripheral vision using attention-guided diffusion models von Raina, Ritik, Ahn, Seoyoung, Zelinsky, Gregory

Volltext

Reconstruction-guided attention improves the object recognition robustness of neural networks von Ahn, Seoyoung, Adeli, Hossein, Zelinsky, Gregory

Volltext

Using Object Reconstruction as a Dynamic Attention Window to Improve Recognition Robustness von Ahn, Seoyoung, Adeli, Hossein, Zelinsky, Gregory

Volltext

A brain-inspired object-based attention network for multi-object recognition and visual reasoning von Adeli, Hossein, Ahn, Seoyoung, Zelinsky, Gregory

Volltext

Object-based association fields for grouping and attention von Adeli, Hossein, Ahn, Seoyoung, Zelinsky, Gregory, Kriegeskorte, Nikolaus

Volltext

Three theories of object-based attention implemented in deep neural network models von Adeli, Hossein, Ahn, Seoyoung, Zelinsky, Gregory, Kriegeskorte, Nikolaus

Volltext

Predicting Mental States from Eye Movements During Reading von Ahn, Seoyoung, Zelinsky, Gregory J.

Volltext

A brain-inspired object-based attention network for multiobject recognition and visual reasoning von Adeli, Hossein, Ahn, Seoyoung, Zelinsky, Gregory J

Volltext

Modeling the dynamics of spreading attention in objects: Do transformers behave like humans? von Adeli, Hossein, Ahn, Seoyoung, Kriegeskorte, Nikolaus, Zelinsky, Gregory

Volltext

Use of superordinate labels yields more robust and human-like visual representations in convolutional neural networks von Ahn, Seoyoung, Zelinsky, Gregory J, Lupyan, Gary

Volltext

Exploring the effects of linguistic labels on learned visual representations using convolutional neural networks von Ahn, Seoyoung, Zelinsky, Gregory, Lupyan, Gary

Volltext

The attentive reconstruction of objects facilitates robust object recognition von Ahn, Seoyoung, Adeli, Hossein, Zelinsky, Gregory J

Volltext

Reward Maps Predict Target-present and Target-absent Visual Search von Zelinsky, Gregory J., Ahn, Seoyoung, Yang, Zhibo, Chen, Yupei, Mondal, Sounak, Hoai, Minh, Samaras, Dimitrios

Volltext

Predicting Visual Attention in Graphic Design Documents von Chakraborty, Souradeep, Wei, Zijun, Kelton, Conor, Ahn, Seoyoung, Balasubramanian, Aruna, Zelinsky, Gregory J., Samaras, Dimitris

Volltext

Look Hear: Gaze Prediction for Speech-Directed Human Attention von Mondal, Sounak, Ahn, Seoyoung, Yang, Zhibo, Balasubramanian, Niranjan, Samaras, Dimitris, Zelinsky, Gregory, Hoai, Minh

Volltext

Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning von Zelinsky, Gregory J, Chen, Yupei, Ahn, Seoyoung, Adeli, Hossein, Yang, Zhibo, Huang, Lihan, Samaras, Dimitrios, Hoai, Minh

Volltext

Target-Absent Human Attention von Yang, Zhibo, Mondal, Sounak, Ahn, Seoyoung, Zelinsky, Gregory, Hoai, Minh, Samaras, Dimitris

Volltext

Reconstruction-guided attention improves the robustness and shape processing of neural networks von Ahn, Seoyoung, Adeli, Hossein, Zelinsky, Gregory J

Volltext bestellen